• Journal of the Korea Concrete Institute
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• v.26 no.6
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• pp.687-694
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• 2014

This study established a displacement ductility ratio model for ductile design for the boundary element of shear walls. To determine the curvature distribution along the member length and displacement at the free end of the member, the distributions of strains and internal forces along the shear wall section depth were idealized based on the Bernoulli's principle, strain compatibility condition, and equilibrium condition of forces. The confinement effect at the boundary element, provided by transverse reinforcement, was calculated using the stress-strain relationship of confined concrete proposed by Razvi and Saatcioglu. The curvatures corresponding to the initial yielding moment and 80% of the ultimate state after the peak strength were then conversed into displacement values based on the concept of equivalent hinge length. The derived displacement ductility ratio model was simplified by the regression approach using the comprehensive analytical data obtained from the parametric study. The proposed model is in good agreement with test results, indicating that the mean and standard deviation of the ratios between predictions and experiments are 1.05 and 0.19, respectively. Overall, the proposed model is expected to be available for determining the transverse reinforcement ratio at the boundary element for a targeted displacement ductility ratio.

• Journal of Korea Multimedia Society
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• v.11 no.4
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• pp.423-433
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• 2008

The surface curvatures extracted from the face contain the most important personal facial information. In particular, the face shape using the depth information represents personal features in detail. In this paper, we develop a method for recognizing the range face images by combining the multiple face regions using fuzzy integral. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area and has to take into consideration of the orientated frontal posture to normalize. Multiple areas are extracted by the depth threshold values from reference point, nose tip. And then, we calculate the curvature features: principal curvature, gaussian curvature, and mean curvature for each region. The second step of approach concerns the application of eigenface and Linear Discriminant Analysis(LDA) method to reduce the dimension and classify. In the last step, the aggregation of the individual classifiers using the fuzzy integral is explained for each region. In the experimental results, using the depth threshold value 40 (DT40) show the highest recognition rate among the regions, and the maximum curvature achieves 98% recognition rate, incase of fuzzy integral.